Assessment of vehicle performance in harsh environments using LSU driving simulator and numerical simulations.

Cai, Steve C. S.; Ishak, Sherif S.; Hu, Jiexuan · 2015 · ROSA P / Southwest Region University Transportation Center (U.S.)

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This study investigates the impact of harsh environmental conditions, specifically strong crosswinds and wet road surfaces, on vehicle performance and driver behavior. The research is motivated by the critical need for effective traffic planning in coastal regions prone to tropical storms and hurricanes, where single-vehicle accidents caused by adverse weather can severely disrupt evacuation routes and daily transportation. While large trucks are known to be vulnerable to wind forces, the authors aim to establish a framework for assessing vehicle safety and driver reactions under hazardous conditions to minimize accident risks and optimize evacuation strategies. The methodology combines numerical simulations with driving simulator experiments. First, Computational Fluid Dynamics (CFD) using sliding mesh technology was employed to simulate the airflow around a sedan, calculating aerodynamic forces and coefficients as functions of the yaw angle between the vehicle and wind direction. This approach allowed for the realistic simulation of relative motion between the vehicle and the road. Second, the Louisiana State University (LSU) driving simulator was modified to reproduce real-time wind loadings based on vehicle and wind velocities. Two drivers participated in tests over ten days, navigating scenarios involving strong crosswinds under both clear and rainy conditions. Data collected included vehicle metrics such as lane offset, velocity, and heading error, as well as driver metrics including reaction time, steering angle, and pedal usage. The results indicate that higher wind speeds significantly increase the variance in lane offset and heading error, leading to larger mean lateral displacements when crosswinds first impact the vehicle. Statistical analysis revealed significant differences (P-value < 0.0001) in vehicle performance metrics, including lane offset, steering angle, and velocity, across different driving environments and days. However, the study found no statistically significant difference (P-value > 0.0001) in vehicle performance between dry and wet road surfaces when excluding wind action, nor did rain significantly influence driver reaction times. The CFD simulations confirmed that vehicle motion affects aerodynamic coefficients, which can be modeled as functions of the yaw angle. The significance of this work lies in demonstrating a feasible approach for studying driver and vehicle behavior in hazardous environments through the integration of CFD and driving simulators. The findings provide a basis for developing statistical models to predict driver behavior and vehicle performance under complex topographic and weather conditions. This framework supports improved highway traffic designs and the optimization of evacuation routes, ultimately aiming to reduce single-vehicle accident risks and enhance transportation resiliency in coastal areas during emergencies.

Key finding

Higher wind speeds lead to larger mean lateral displacement and greater lane offset variance, while road surface conditions (dry vs. wet) showed no significant effect on vehicle performance excluding wind action.

Methodology

simulator

Sample size: 2

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• simulator validity fidelity

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Methodological Resource: tool software, validation psychometrics
• Theoretical Contribution: computational model